Service
AI Security
AI has opened a new front most defenses haven't caught up to. We test how your AI systems can be manipulated, misused, or turned against you — and help you deploy them without opening a door you can't see.
What we test
The attack surface AI creates.
AI systems introduce attack vectors that traditional security testing doesn't cover. Prompt injection, model manipulation, data exfiltration through inference, and misuse of AI-powered features are real, exploitable risks — and most organizations haven't tested for them.
- Prompt injection — direct and indirect attacks against LLM-powered applications
- Jailbreaking and guardrail bypass — testing the limits of your model's safety controls
- Data exfiltration via inference — extracting training data or sensitive context through model outputs
- Agentic AI systems — testing autonomous agents with tool access and real-world capabilities
- RAG and retrieval systems — poisoning, manipulation, and unauthorized data access
- AI-powered feature abuse — misuse of AI features in your products to cause unintended behavior
How we do it
Adversarial testing built for AI systems.
AI security requires a different methodology than traditional penetration testing. We combine adversarial ML techniques with application security expertise — testing your AI systems the way a sophisticated attacker would, not the way a benchmark does.
01
System mapping
Understanding your AI architecture — models, data flows, integrations, and trust boundaries — before any adversarial testing begins.
02
Threat modeling
Identifying the attack vectors most relevant to your specific deployment: what an attacker could gain, and what paths lead there.
03
Adversarial testing
Systematic testing of identified attack vectors — prompt injection, model manipulation, data extraction, and agentic abuse scenarios.
04
Risk assessment
Findings contextualized by real-world exploitability and business impact — not just theoretical vulnerability classification.
What you get
Findings your team can act on.
AI security findings require context that traditional vulnerability reports don't provide. We explain what we found, why it matters in your specific deployment, and what to do about it — in language your security team and your AI team both understand.
Attack narrative
A clear account of what we tested, what we found, and what an attacker could do with it — grounded in your actual deployment, not generic AI risk.
Technical findings
Each vulnerability documented with reproduction steps, impact assessment, and specific remediation guidance for your architecture.
Deployment guidance
Recommendations for guardrails, monitoring, input validation, and architectural changes that reduce your AI attack surface going forward.
Remediation support
AI security fixes are often novel — there's no patch to apply. We stay available to work through remediation with your team.
Test your AI before someone else does.
Most AI deployments haven't been tested adversarially. A scoping conversation takes 30 minutes and tells you whether you have exposure worth testing.